1. Field of the Invention
The present invention relates to telecommunications. More particularly, the present invention relates to soft decision decoding (SDD) algorithms for wireless or wired digital telecommunication systems having variable parameters and/or selective fading.
2. State of the Art
Digital telecommunication systems typically contain transmitters and receivers. Error control coding is an important procedure of the transmission process, and an error control codec is an important part of the system. The codec consists of an encoder in the transmitter and a decoder in the receiver.
There are different error correction and error detection codes, and each of them can be decoded with several decoding algorithms. Two major classes of decoding algorithms are: hard decision decoding (HDD) and soft decision decoding (SDD) algorithms. According to HDD, the receiver first determines the identity of each transmitted symbol (maybe erroneously), i.e., the receiver makes hard decision. Then, the sequence of received symbols are decoded; i.e., the decoder determines a corrected sequence of the transmitted symbols. In contrast, according to SDD, the receiver first estimates some measure of reliability of each possible decision without making decisions about the transmitted symbol at all; i.e., the decoder makes a soft decision. Then a sequence of estimated reliabilities (soft decisions) are decoded so that the decoder determines a corrected sequence of the transmitted symbols.
HDD and SDD approaches are generally illustrated in FIG. 1, where transmitted binary signals are shown as points “0” and “1”, and the received signal as point R. When using HDD, the receiver makes decision in favor of point 0, because the received signal R is closer to point 0 than to point 1, and this hard decision 0 will be used in the HDD decoder. When using SDD, the receiver does not make hard decision at all, but calculates two distances d(0,R) and d(1,R) between the received signal R and reference signals 0 and 1, respectively. Distances d(0,R) and d(1,R) may serve as measure of reliability of the received signal. If, for example, d(0,R)=d(1,R), the received symbol is completely unreliable; i.e., it does not bear any information about the transmitted signal. On the other hand, if d(0,R) or d(1,R) is close to zero, the received symbol is very reliable. Soft decisions d(0,R) and d(1,R) or their combination will be further used in the SDD decoder.
Historically, HDD was the first decision coding technique utilized because its implementation is much easier than the SDD implementation. However, it was well known that SDD could provide much better performance in terms of bit error rate. Presently, SDD is the more commonly utilized decoder implementation because it is the most efficient way to achieve the highest data rate with required performance. SDD is used in wired ADSL systems (i.e.G.992.1), in wireless local area network (WLAN) systems (IEEE 802.11a standard), in wireless local loop (WLL) systems (IEEE 802.16 standard) and other wired and wireless applications. It is also recommended for future 3G and 4G wireless mobile systems, possibly, in combination with Orthogonal Frequency Division Multiplexing (OFDM) and Multi-input-Multi-output (MIMO) technologies.
Measurement of the received symbol reliability; i.e., the SDD metric, is used with different decoding algorithms such as the Viterbi algorithm for convolution codes, the Soft Output Viterbi algorithm (SOVA) for Turbo codes, and iterative probabilistic algorithms for LDPC and Turbo codes. In any case, a problem remains in finding an appropriate SDD metric, which, on the one hand, provides the optimal decoding, and, on the other hand, can be easily implemented.
In an additive white Gaussian noise (AWGN) channel with constant parameters, the best SDD metric is based on Euclidean distances between the received signal and reference signals. For channels with variable parameters such as radio channels with selective fading, squared distances between the received signal and properly scaled reference signals are recommended in the literature as the optimal SDD metric. See, e.g., B. Vucetic, J. Yuan, “Turbo codes”, section 8.5.1, Kluwer Academic Publishers, 2001. This approach, however, is difficult to implement because it requires scaling of all reference signals for each received signal element, for example, for each carrier in an orthogonal frequency division multiplexed (OFDM) system. Therefore, as a rule, in practice for AWGN channels, a simplified metric is used which is based on distances between the properly scaled received signal and reference signals. This metric however is not optimal for selective fading channels.